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Data Warehousing and Mining Semester 8 (BE Fourth Year) BE Computer Engineering University of Mumbai Topics and Syllabus

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University of Mumbai Syllabus For Semester 8 (BE Fourth Year) Data Warehousing and Mining: Knowing the Syllabus is very important for the students of Semester 8 (BE Fourth Year). Shaalaa has also provided a list of topics that every student needs to understand.

The University of Mumbai Semester 8 (BE Fourth Year) Data Warehousing and Mining syllabus for the academic year 2021-2022 is based on the Board's guidelines. Students should read the Semester 8 (BE Fourth Year) Data Warehousing and Mining Syllabus to learn about the subject's subjects and subtopics.

Students will discover the unit names, chapters under each unit, and subtopics under each chapter in the University of Mumbai Semester 8 (BE Fourth Year) Data Warehousing and Mining Syllabus pdf 2021-2022. They will also receive a complete practical syllabus for Semester 8 (BE Fourth Year) Data Warehousing and Mining in addition to this.

CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

University of Mumbai Semester 8 (BE Fourth Year) Data Warehousing and Mining Revised Syllabus

University of Mumbai Semester 8 (BE Fourth Year) Data Warehousing and Mining and their Unit wise marks distribution

University of Mumbai Semester 8 (BE Fourth Year) Data Warehousing and Mining Course Structure 2021-2022 With Marking Scheme

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C Introduction to Data Warehousing
  • The Need for Data Warehousing
  • Increasing Demand for Strategic Information
  • Inability of Past Decision Support System
  • Operational V/s Decisional Support System
  • Data Warehouse Defined
  • Benefits of Data Warehousing
  • Features of a Data Warehouse
  • The Information Flow Mechanism
  • Role of Metadata
  • Classification of Metadata
  • Data Warehouse Architecture
  • Different Types of Architecture
  • Data Warehouse and Data Marts
  • Data Warehousing Design Strategies
CC Dimensional Modeling
  • Data Warehouse Modeling Vs Operational Database Modeling
  • Dimensional Model Vs ER Model
  • Features of a Good Dimensional Model
  • The Star Schema
  • How Does a Query Execute?
  • The Snowflake Schema
  • Fact Tables and Dimension Tables
  • The Factless Fact Table
  • Updates To Dimension Tables:- Slowly Changing Dimensions, Type 1 Changes, Type 2 Changes, Type 3 Changes, Large Dimension Tables, Rapidly Changing or Large Slowly Changing Dimensions, Junk Dimensions
  • Keys in the Data Warehouse Schema, Primary Keys, Surrogate Keys & Foreign Keys
  • Aggregate Tables
  • Fact Constellation Schema or Families of Star
CCC ETL Process
  • Challenges in ETL Functions; Data Extraction; Identification of Data Sources; Extracting Data: Immediate Data Extraction, Deferred Data Extraction
  • Data Transformation:- Tasks Involved in Data Transformation
  • Data Loading:- Techniques of Data Loading, Loading the Fact Tables and Dimension Tables Data Quality
  • Issues in Data Cleansing
CD Online Analytical Processing (OLAP)
  • Need for Online Analytical Processing
  • OLAP and Multidimensional Analysis
  • Hypercubes
  • OLAP Operations in Multidimensional Data Model
D Introduction to Data Mining
  • What is Data Mining
  • Knowledge Discovery in Database (KDD)
  • What can be Data to be Mined
  • Related Concept to Data Mining
  • Data Mining Technique
  • Application and Issues in Data Mining
DC Data Exploration
  • Types of Attributes
  • Statistical Description of Data
  • Data Visualization
  • Measuring similarity and dissimilarity
DCC Data Preprocessing
  • Why Preprocessing?
  • Data Cleaning; Data Integration; Data Reduction: Attribute subset selection, Histograms, Clustering and Sampling; Data Transformation & Data Discretization:- Normalization, Binning, Histogram Analysis and Concept hierarchy generation.
DCCC Classification
801 Basic Concepts
  1. Classification methods:-
  • Decision Tree Induction:- Attribute Selection Measures, Tree pruning.
  • Bayesian Classification:- Naïve Bayes’ Classifier.
802 Prediction
  • Structure of regression models
  • Simple linear regression, Multiple linear regression.
803 Model Evaluation and Selection
  • Accuracy and Error measures, Holdout, Random Sampling, Cross Validation, Bootstrap
  • Comparing Classifier performance using ROC Curves
804 Combining Classifiers
  • Bagging, Boosting, Random Forests.
CM Clustering
  • What is clustering?
  • Types of data
  • Partitioning Methods (K-Means, KMedoids)
  • Hierarchical Methods(Agglomerative, Divisive, BRICH)
  • Density-Based Methods (DBSCAN, OPTICS)
M Mining Frequent Pattern and Association Rule
  • Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules
  • Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, A pattern growth approach for mining Frequent Itemsets
  • Mining Frequent itemsets using vertical data formats
  • Mining closed and maximal patterns
  • Introduction to Mining Multilevel Association Rules and Multidimensional Association Rules
  • From Association Mining to Correlation Analysis, Pattern Evaluation Measures
  • Introduction to Constraint-Based Association Mining
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